Epidemiology of Chronic Pain with Psychological Comorbidity: Prevalence, Risk, Course, and Prognosis
Bibliographic record
Abstract
OBJECTIVE: To review the relation between chronic pain and psychological comorbidities, and the influence on course and prognosis, based on epidemiologic and population studies. METHOD: We present a narrative overview of studies dealing with the epidemiology of chronic pain associated with mental health and psychiatric factors. Studies were selected that were of good quality, preferably large studies, and those that dealt with prevalences, course and prognosis of chronic pain, risk factors predicting new pain and comorbid disorders, and factors that affect health outcomes. RESULTS: Chronic pain is a prevalent condition, and psychological comorbidity is a frequent complication that significantly changes the prognosis and course of chronic pain. In follow-up studies, chronic pain significantly predicts onset of new depressions, and depression significantly predicts onset of new chronic pain and other medical complaints. Age, sex, severity of pain, psychosocial problems, unemployment, and compensation are mediating factors in course and prognosis. CONCLUSION: In assessment of chronic pain, the evidence from epidemiologic studies makes it clear that chronic pain can best be understood in the context of psychosocial factors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".